AI/ML Engineer

What you'll do

• Build, train, and deploy deep learning and classical ML models on large-scale financial and behavioral datasets.
• Own the end-to-end ML lifecycle including problem framing, training, evaluation, deployment, monitoring, and iteration.
• Design and improve ML systems such as feature pipelines, batch and real-time inference, and retraining workflows.
• Translate ambiguous business problems into technical ML solutions in partnership with Product, Engineering, and Risk teams.
• Connect model performance to business metrics and use experimentation to prove measurable impact on member outcomes.
• Contribute to the shared ML platform, tooling, and standards used by the broader engineering organization.
• Identify opportunities where AI/ML creates measurable impact versus where simpler solutions are more appropriate.

What we're looking for

• Experience building and deploying deep learning models in production with a grasp of architecture, training dynamics, and evaluation.
• Solid machine learning fundamentals including classical modeling, evaluation design, and metric selection.
• Hands-on experience across the end-to-end ML lifecycle including training, experimentation, optimization, deployment, and monitoring.
• Ability to work with messy real-world data including label definition, leakage, class imbalance, and train/serve skew.
• Strong proficiency in Python and SQL with deep learning frameworks like PyTorch and distributed compute like Spark or PySpark.
• Working knowledge of modern ML infrastructure including AWS, SageMaker, Airflow, Kafka, Redis, and Snowflake.
• Experience in Trust & Safety, Lending, Growth, or Foundation Models (preferred).